In the field of medical healthcare, it is usual to generate medical reports where a respective physician dictates the report to generate a speech file in a computer system, which speech file is then automatically converted into a text file by using a speech recognition system; the transcribed text file usually is manually corrected and checked to create the final medical report document.
After a medical report is completed, it is often necessary, or even prescribed, that the report “is coded” according to strict guidelines so that e.g. an insurance company or a government organization can be billed for payment. Each type of medical service provided, prescriptions made, referrals cited must be identified from the report text itself accordingly. The coding guidelines are strict and very complex.
Delivering quality healthcare depends on capturing accurate and timely medical data. Medical coding professionals fulfill this need as key players in the healthcare workplace.
At present, health information coding is the transformation of verbal descriptions of diseases, injuries, procedures etc., generally referred to as “condition information” in this context, into numeric or alphanumeric designations. Originally, medical coding was performed to classify mortality (cause of death) data on death certificates. However, coding is also used to classify morbidity and procedural data. The coding of health-related data permits access to medical records by diagnoses and procedures for use in clinical care, research, and education.
Since the implementation of automatic billing on the basis of coded medical reports, there has been much more emphasis placed on medical coding. Currently, reimbursement of hospital and physician claims for medicare patients depends entirely on the assignment of codes to describe diagnoses, services, and procedures provided. To overcome the problem of healthcare fraud and abuse, as the basis for reimbursement, appropriate and accurate medical coding has become crucial as healthcare providers seek to assure compliance with official coding guidelines.
There are many demands for accurately coded data from the medical record. In addition to their use on claims for reimbursement, codes are included on data sets used to evaluate the processes and outcomes of healthcare. Code data are also used internally by institutions for quality management activities, case-mix management, planning, marketing and other administrative and research activities.
Currently, the coding process is either manual, or it is done in an extra processing step semi-automatically using a text parsing tool (also using a “natural language processing” (“NLP”) or “semantic web” technology), compare for instance U.S. Pat. No. 6,915,254 B1 where a system for automatically assigning medical codes using NLP is described. Of course, manual coding is very cumbersome and lengthy and expensive, since properly trained coding personnel is very scarce. On the other hand, the text parsing system using NLP is error prone since it has to analyze human readable text, which is often vague. Additionally, it is to be considered that there is a shortage of certified medical coders in hospitals, physician practices, and other healthcare facilities. According to the United States Bureau of Labor, employment of medical record and health information technicians is expected to grow much faster than the average field.
From US 2003/0154085 A1, it is already known to use predetermined electronic templates to be filled out when generating medical reports under the assistance of computer means. In particular, a specific, suitable template apt for the specific patient and his condition is selected by the physician, and personal data of the respective patient, as name, address, age, sex etc., are automatically inserted into this template, such personal data being already available from a hospital information system (HIS). Then, the physician dictates his specific text using an interactive voice interface of the computer system, for describing a particular diagnosis, procedure, medication etc., as appropriate. This speech file is automatically converted into a text file by speech recognition.
During dictation, the system compares the speech input with predetermined terms or phrases stored in a database, to match the audio input with such terms and phrases, that is to determine whether the audio input would be apt for later automatic NLP coding; in the case of a lack of match, the physician is requested to repeat or clarify his audio input, to arrive at a match.
Apparently, also this prior art system is cumbersome, lengthy and expensive.